Principles, Models, and Methods for the Characterization and Analysis of Lurkers in Online Social Networks

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Principles, models, and methods for the characterization and analysis of lurkers in online social networks

Roberto Interdonato, Andrea Tagarelli

DIMES Dept., Università della Calabria, Italy

The 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

  • Lurking in OSNs: Principles, Models, and Methods
  • Lurking in OSNs: Principles, Models, and Methods

Lurk(er): what meanings

Lurking in OSNs: Principles, Models, and Methods

“Lurker”: let’s google it …

Lurking in OSNs: Principles, Models, and Methods

Lurk(er): what meanings

Lurking in OSNs: Principles, Models, and Methods

Outline

2.ꢀ Modeling lurking behaviors

1.ꢀ Lurking in online communities

ꢀ Topology-driven lurking definition

ꢀ The issue of controversial definitions

3.ꢀ Lurker ranking methods

ꢀ Lurking and online behavioral models

4.ꢀ Experimental evaluation

ꢀ Static scenarios

ꢀ The opportunity of de-lurking

ꢀ Dynamic scenarios

5.ꢀ Applications to other domains

ꢀ Vicariously learning ꢀ Lurking in social trust contexts

6.ꢀ Delurking via Targeted Influence
Maximization

ꢀ The DEvOTION algorithm

7.ꢀ Conclusion and future work

Lurking in OSNs: Principles, Models, and Methods

LURKING IN ONLINE COMMUNITIES

Lurking in OSNs: Principles, Models, and Methods

The 1:9:90 rule of participation inequality (1/3)

Arthur, C. (2006). What is the 1% rule? In: The guardian. UK: Guardian News and Media.

Lurking in OSNs: Principles, Models, and Methods

The 1:9:90 rule of participation inequality (2/3)

•ꢀ [Nonnecke & Preece, 2000] Email-based discussion lists:

•ꢀ 77 online health support groups and 21 online technical support groups •ꢀ 46% of the health support group members and 82% of the technical support group members are lurkers

•ꢀ [Swartz, 2006] On Wikipedia: over 50% of all the edits are done by only
0.7% of the users

•ꢀ [van Mierlo, 2014] On four DHSNs (AlcoholHelpCenter,
DepressionCenter, PanicCenter, and StopSmokingCenter):

•ꢀ 63,990 users, 578,349 posts •ꢀ Lurkers account for 1.3% (n=4668), Contributors for 24.0% (n=88,732), and
Superusers for 74.7% (n=276,034) of content

Nonnecke, B., Preece, J. (2000). Lurker Demographics: Counting the Silent. In Proc. SIGCHI Human Factors in Computing. Swartz, A. (2006). Raw thought: Who writes Wikipedia. Blog article at www.aaronsw.com/weblog/whowriteswikipedia. van Mierlo, T. (2014). The 1% rule in four digital health social networks: An observational study. Medical Internet Research, 16(2).

Lurking in OSNs: Principles, Models, and Methods

The 1:9:90 rule of participation inequality (3/3)

•ꢀ Online learning courses:

•ꢀ No relation between interactivity (i.e., posting) and learning (i.e., earned grade) •ꢀ Extend the notion of interactivity to include the lurking activity

•ꢀ Each of the 128 students reads at least one contribution •ꢀ 62% of the class are lurkers—only reading posts, not contributing anything

•ꢀ No correlation between the no. of readers and the no. of writers •ꢀ Every participant, active or lurking, reads more postings than they write

•ꢀ Active participation in an online discussion list, based on passive lurking, is expressed by reading, reflecting on the contribution of all the other members

Ebner, M., Holzinger, A. (2005). Lurking: An underestimated human-computer phenomenon. IEEE Multimedia, 12(4), 70–75.

Lurking in OSNs: Principles, Models, and Methods

Perception of lurking (1/2)

•ꢀ Lurkers as “free-riders” [Kollock & Smith,1996; Morris & Ogan, 1996; Wellman
& Gulia,1999; Rheingold, 2000]

•ꢀ Sustainability of an online community

•ꢀ Fresh content and timely interactions •ꢀ Lurkers contribute little value [van Mierlo, 2014]

•ꢀ Lurkers may impair the virality of the community [Nielsen, 2011]

Kollock, P., Smith, M. (1996). Managing the virtual commons. Computer-mediated communication: Linguistic, social, and cross- cultural perspectives, 109–128.

Morris, M., Ogan, C. (1996). The internet as mass medium. Journal of Communication, 46(1), 39–50. Wellman, B., Gulia, M. (1999). Net surfers don’t ride alone: Virtual communities as communities. Networks in the Global Village, 331– 366.

Rheingold, H. (2000). The virtual community: Homesteading on the electronic frontier. MIT Press.

Nielsen, J. (2011). Participation inequality: Encouraging more users to contribute, http://www.useit.com/alertbox/ participation_inequality.html.

Lurking in OSNs: Principles, Models, and Methods

Perception of lurking (2/2)

•ꢀ Most lurkers are NOT free-riders (e.g., [Nonnecke, Preece, & Andrews, 2004; Nonnecke,
Andrews, & Preece, 2006])

•ꢀ Lurking can be regarded as passive participation that permits inclusion [Ferree, 2002] •ꢀ Lurking is normal and an active, participative and valuable form of online behavior [Edelmann,

2013]

•ꢀ Lurkers perceive themselves as community members [Nonnecke et al., 2006] •ꢀ Lurking as a form of cognitive apprenticeship: “legitimate peripheral participation” [Lave &

Wenger, 1999]

Nonnecke, B., Preece, J., Andrews, D. (2004). What lurkers and posters think of each other. In Proc. the 37th annual Hawaii Int. Conf. on System Sciences. Nonnecke, B., Andrews, D., Preece, J. (2006). Non-public and public online community participation: Needs, attitudes and behavior. Electronic Commerce

Research, 6(1), 7–20. Ferree, M. M., Gamson, W. A., Gerhards, J., Rucht, D. (2002). Shaping abortion discourse: Democracy and the public sphere in Germany and the United

States. New York, Cambridge University Press. Edelmann, N. (2013). Reviewing the definitions of ‘‘Lurkers’’ and some implications for online research. Cyberpsychology, Behavior, and Social Networking, 16(9), 645–649.

Lave, J., Wenger, E. (1999). Legitimate peripheral participation. Learners, learning and assessment. London: The Open University, pp. 83–89.

Lurking in OSNs: Principles, Models, and Methods

How to identify lurkers(1/4)

•ꢀ Two main features: seldom posting, mostly reading contents

•ꢀ Attempts to set quantitative standards:

•ꢀ “never post in an online community” [Nonnecke et al., 2006] •ꢀ “post messages only once in a long while” [Golder & Donath, 2004] •ꢀ “no contribution during a 3-month period” [Nonnecke & Preece, 2000] •ꢀ “#posts<4 from the beginning, or never posted in the last 4 months” [Ganley et al., 2012]

•ꢀ Accounting for the “login” dimension [Chen, 2004]

•ꢀ Lurkers log into the community every week throughout a 6-week timespan

Golder, S. A., Donath, J. (2004). Social roles in electronic communities. Internet Research, 5, 19–22. Ganley, D., Moser, C., Groenewegen, P. (2012). Categorizing behavior in online communities: A look into the world of cake bakers. In Proc. HICSS, pp. 3457–3466. Chen, F. C. (2004). Passive forum behaviors (lurking): A community perspective. In Proc. 6th Int. Conf. on Learning Sciences, pp. 128–135.

Lurking in OSNs: Principles, Models, and Methods

How to identify lurkers(2/4)

•ꢀ Find a certain percentage of most non-active users as lurkers

•ꢀ e.g., [Rau et al., 2008] On Microsoft’s Wallop SNS, 40% of the most non-active as lurkers

•ꢀ Two continuous dimensions (participation pattern) [Leshed, 2005]:

•ꢀ Publicity: ratio of public (i.e., posting) to non-public (i.e., reading) activities •ꢀ Intensity: the frequency of total activities performed by a member

•ꢀ Lurkers tend to have higher intensity and lower publicity

Rau, P.-L. P., Gao, Q., Ding, Y. (2008). Relationship between the level of intimacy and lurking in online social network services. Computers in Human Behavior, 24(6), 2757–2770. Leshed, G. (2005). Posters, lurkers, and in between: A multidimensional model of online community participation patterns. In Proc. HIC.

Lurking in OSNs: Principles, Models, and Methods

How to identify lurkers(3/4)

•ꢀ Lurkers may be classified into: [Takahashi et al. 2003; Walker et al. 2013]

•ꢀ Passive lurkers: only read for their use •ꢀ Active lurkers: for propagation, practical use, or personal contact

•ꢀ Lurkers vs. “non-users” [Springer et al. 2015]

•ꢀ Lurking as passive participation, as opposed to commenting (active participation) •ꢀ Non-users: read news but have no interest in the user comments/discussions

Takahashi, M., Fujimoto, M., Yamasaki, N. (2003). The active lurker: Influence of an in-house online community on its outside environment. In Proc. ACM

SIGGROUP Conf. on Supporting Group Work, pp. 1–10.

Walker, B., Redmond, J., Lengyel, A. (2013). Are they all the same? Lurkers and posters on the net. eCULTURE, 3(1). Springer, N., Engelmann, I., Pfaffinger, C. (2015). User comments: motives and inhibitors to write and read. Information, Communication & Society, 18(7): 798-815

Lurking in OSNs: Principles, Models, and Methods

How to identify lurkers(4/4)

•ꢀ Can we generalize using the previously discussed criteria?

•ꢀ No, it depends on the size, topics and culture of the online community! •ꢀ Many factors influence online behaviors (e.g., [Bishop, 2007; Fan et al., 2009]):

•ꢀ Environmental influences •ꢀ Personal characteristics •ꢀ Organizational commitment

•ꢀ Many lurkers: good or bad?

•ꢀ Active lurkers are beneficial for the propaganda and development of the community •ꢀ but they have low posting rate and lack of valuable content •ꢀ Emergence for strategies to promote de-lurking

Bishop, J. (2007). Increasing participation in online communities: A framework for human–computer interaction. Computers in Human Behavior, 23(4), 1881–1893. Fan, Y.-W., Wu, C.-C., Chiang, L.-C. (2009). Knowledge sharing in virtual community: The comparison between contributors and lurkers. In Proc. Int. Conf. on

Electronic Business, pp. 662–668.

Lurking in OSNs: Principles, Models, and Methods

Lurking and online behavioral models(1/2)

Environmental factors that affect the user’s feeling and the user’s willingness to contribute

Development and spread of community norms, Contribution of valuable resources, and Consumption of resources

Personal characteristics of the users
Factors based on the relationships between the users and the community

Sun, N., Rau, P. P.-L., Ma, L. (2014). Understanding lurkers in online communities: A literature review.

Computers in Human Behavior, 38, 110–117.

Lurking in OSNs: Principles, Models, and Methods

Lurking and online behavioral models(2/2)

Individual factors that influence online behaviors

Sun, N., Rau, P. P.-L., Ma, L. (2014). Understanding lurkers in online communities: A literature review.

Computers in Human Behavior, 38, 110–117.

Lurking in OSNs: Principles, Models, and Methods

Why lurkers lurk (1/4)

•ꢀ Four main motivational factors [Sun et al., 2014]:

1.ꢀ Environmental influence determined by the online community 2.ꢀ Personal preference related to an individual’s personality 3.ꢀ Relationships between the individual and the community 4.ꢀ Security considerations

Sun, N., Rau, P. P.-L., Ma, L. (2014). Understanding lurkers in online communities: A literature review.

Computers in Human Behavior, 38, 110–117.

Lurking in OSNs: Principles, Models, and Methods

Why lurkers lurk (2/4)

1. Environmental influence

•ꢀ Bad usability/interaction design •ꢀ “Too many or too few messages to deal with” •ꢀ Poor quality of the posted contents [Springer et al., 2015]

•ꢀ Negatively influences the affective/entertainment dimension of gratification sought

•ꢀ “Don’t know how to post”

•ꢀ [Nonnecke et al., 2004] Survey of 1188 users from 375 MSN online communities: 7.8% of lurkers

•ꢀ caused by poor usability and insufficient usage guidance

•ꢀ Low response rate and long response delay •ꢀ Low reciprocity

•ꢀ [Fan et al., 2009] Survey with 207 valid responses (74% of lurkers) •ꢀ Leads to think that “posting has no value to me”

•ꢀ “Others respond the way I would” •ꢀ “Just reading/browsing is enough”, “No requirement to post”

•ꢀ [Kucuk, 2010] Survey of 1078 online course students: 31.1% of lurkers

Nonnecke, B., Preece, J., Andrews, D., Voutour, R. (2004). Online lurkers tell why. In Proc. AMCIS. Kücük, M. (2010). Lurking in online asynchronous discussion. Procedia-Social and Behavioral Sciences, 2(2), 2260–2263.

Lurking in OSNs: Principles, Models, and Methods

Why lurkers lurk (3/4)

2. Personal reasons

•ꢀ Introversion, lack of self-efficacy, bashfulness [Nonnecke et al., 2004] •ꢀ Lack of confidence in the ability to post [Lee al., 2006]

•ꢀ 40% of inactive students of an online program [Beaudoin, 2002]

•ꢀ “Don’t feel comfortable writing ideas online”

•ꢀ 25% of inactive students of an online program [Beaudoin, 2002]

•ꢀ No need to post – only seeking for information •ꢀ Nothing to post or lack of expertise •ꢀ “Others had already posted similarly” •ꢀ Time constraints •ꢀ Missing opportunity to earn money (e.g., with commenting activities) [Springer et al., 2015]

Lee, Y.-W., Chen, F.-C., Jiang, H.-M. (2006). Lurking as participation: A community perspective on lurkers’ identity and negotiability. In

Proc. Conf. on Learning Sciences, pp. 404–410.

Beaudoin, M. F. (2002). Learning or lurking?: Tracking the ‘‘invisible’’ online student. The Internet and Higher Education, 5(2), 147–155.

Lurking in OSNs: Principles, Models, and Methods

Why lurkers lurk (4/4)

3. Relationships reasons

•ꢀ Low verbal and affective intimacy with other members

•ꢀ Social penetration theory [Altman & Taylor, 1973]: intimacy develops over time to the extent that members reciprocate disclosures

•ꢀ Lack of commitment to the group •ꢀ Fear making a commitment •ꢀ Don’t want to spend too much time/resources to maintain a commitment

4. Security reasons

•ꢀ Worrying about that posting will violate privacy [Nonnecke & Preece, 2001; Springer et al.,

2015

•ꢀ The community does not satisfy requirements of security and privacy, at different levels

[Wang et al., 2011]

Altman, I., Taylor, D. A. (1973). Social penetration: The development of interpersonal relationships. New York: Holt, Rinehart & Winston. Wang, Y., Norice, G., Cranor, L. F. (2011). Who is concerned about what? A study of American, Chinese and Indian users’ privacy concerns on social network sites. In Proc. Trust and Trustworthy Computing, pp. 146–153. Springer.

Lurking in OSNs: Principles, Models, and Methods

The challenge of “de-lurking”

Provide an environment that makes people’s lives easier

Lurking in OSNs: Principles, Models, and Methods

How to promote de-lurking(1/3)

•ꢀ External stimuli Social Exchange theory [Thibaut & Kelley, 1959]

•ꢀ Providing rewards and removing negative consequences will strengthen intentions •ꢀ Main actions:

Thibaut, J. W., & Kelley, H. H. (1959). The social psychology of groups (Vol. XIII). Oxford, England: John Wiley.

•ꢀ Tangible or intangible rewards •ꢀ Controlling or informative rewards

•ꢀ Encouragement to participate [Nonnecke et al., 2004; Du, 2006]

•ꢀ Helps to set up a pro-sharing norm •ꢀ Enhances users’ commitment to the community •ꢀ Improves users’ confidence in expressing themselves •ꢀ Make lurkers understand the necessity of their contribution •ꢀ Main actions:

•ꢀ Welcome statements, introduction of reward rules, support for browsing and praise for the moderator

Sun, N., Rau, P. P.-L., Ma, L. (2014). Understanding lurkers in online communities: A literature review.

Computers in Human Behavior, 38, 110–117.

Lurking in OSNs: Principles, Models, and Methods

How to promote de-lurking(2/3)

•ꢀ Guidance for newcomers [Du, 2006]

•ꢀ Newcomers are likely to lurk for a while to learn the culture of the community •ꢀ Help from elder/master users •ꢀ Periodically provide opportunities to join conversations

•ꢀ Usability improvement [Nonnecke et al., 2004, 2006; Du, 2006]

•ꢀ Simplify the procedures to send/respond messages •ꢀ Rearranging the presentation of messages

Sun, N., Rau, P. P.-L., Ma, L. (2014). Understanding lurkers in online communities: A literature review.

Computers in Human Behavior, 38, 110–117.

Lurking in OSNs: Principles, Models, and Methods

How to promote de-lurking(3/3)

•ꢀ Usability improvement [Nazi et al., 2015]

•ꢀ Simplify the task of product/service reviewing •ꢀ Given:

•ꢀ User feedback in textual form •ꢀ A user and an item to review

•ꢀ Goal:

•ꢀ Recommend a set of meaningful terms (i.e., tags) to the user

•ꢀ Method:

•ꢀ Extraction of key tags from available reviews according to:

•ꢀ Relevance, Coverage, and Polarity properties

•ꢀ Formulation of top-k meaningful tags identification

•ꢀ Independent Coverage TagAdvisor •ꢀ Dependent Coverage TagAdvisor

Nazi, A., Das, M., Das, G. (2015). The TagAdvisor: Luring the Lurkers to Review Web Items. In Proc. ACM

SIGMOD 2015, 531–543.

Lurking in OSNs: Principles, Models, and Methods

Lurking as a computational problem(1/2)

•ꢀ Hot topic in social science and computer-human interaction

•ꢀ Lurking conceptualized in relation to cultural capital [Soroka & Rafaeli, 2006], boundary spanning and knowledge brokering activities [Craneeld et al., 2011], group learning [Chen &

Chang, 2011], epistemic curiosity [Schneider et al., 2013]

•ꢀ Focus on the identification of insights that might drive empirical evaluation of lurkers’ traits

•ꢀ Also becoming mature in computer science

•ꢀ Classification methods for actors in an OSN [Fazeen et al., 2011]

•ꢀ including lurkers, although treated marginally

•ꢀ Active and passive lifetime [Lang & Wu, 2013]

•ꢀ the latter however requires to know the user‘s last login date

Soroka, V., Rafaeli, S. (2006). Invisible participants: how cultural capital relates to lurking behavior. In Proc. ACM WWW, pp. 163-172. Chen, F. C., Chang, H. M. (2011). Do lurking learners contribute less?: a knowledge co-construction perspective. In Proc. C&T, pp. 169-178. Craneeld, J., Yoong, P., Hu, S. L. (2011). Beyond Lurking: The Invisible Follower-Feeder In An Online Community Ecosystem. In Proc. PACIS. Schneider, A., von Krogh, G., Jager, P. (2013). “What's coming next?" Epistemic curiosity and lurking behavior in online communities. Computers in Human Behavior 29, 293-303 Fazeen, M., Dantu, R., Guturu, P. (2011) Identication of leaders, lurkers, associates and spammers in a social network: context-dependent and context-independent approaches.

Social Netw. Analys. Mining 1(3), 241-254

Lang, J., Wu, S. F. (2013). Social network user lifetime. Social Netw. Analys. Mining 3(3), 285-297.

Lurking in OSNs: Principles, Models, and Methods

Lurking as a computational problem(2/2)

•ꢀ Emergence for computational models, methodologies, and algorithms for

•ꢀ Understanding lurking behaviors to improve

•ꢀ User modeling, personalization and adaptation

•ꢀ Utilizing the mined knowledge in next-generation

•ꢀ Marketing-oriented applications •ꢀ E-learning platforms •ꢀ Collaborative systems •ꢀ Trust systems

Lurking in OSNs: Principles, Models, and Methods

Next …

Modeling lurking behaviors

Evaluation on Twitter, FriendFeed, Flickr, Google+, and Instagram

•ꢀ Reciprocity, preferential attachment

•ꢀ Delurking-oriented randomization model •ꢀ Percolation/resilience analysis

Lurking over time

Topology-driven definition of lurking In-, Out-, and InOut-neighbors driven ranking

methods

Vicariously Learning on RCNs

•ꢀ Lurkers vs. inactive users , and newcomers •ꢀ Responsiveness

•ꢀ VLRank methods

Lurking and Social Trust

•ꢀ Trust-biased LurkerRank methods

•ꢀ Preferential attachment •ꢀ Temporal trends and clustering •ꢀ Topic evolution

Delurking via Targeted Influence Maximization

•ꢀ The DEvOTION method

Lurking in OSNs: Principles, Models, and Methods

A. Tagarelli, R. Interdonato (2014) Lurking in Social Networks: Topology-based Analysis and Ranking Methods.

Soc. Netw. Analys. Mining (SNAM)

A. Tagarelli, R. Interdonato (2013) “Who’s out there?” Identifying and Ranking Lurkers in Social Networks.

In Pr oc. ASONAM’13

MODELING LURKING BEHAVIORS

•ꢀ In-degree, Out-degree and Lurking •ꢀ Topology-driven Lurking definition

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    The Real Time Web Explained With A Real World Example Date: 21. Oktober Autor: Christian Stocker, Liip AG TechTalk The Real Time Web Instant Notifications “Real Time” Search No more PULL A lot of Buzz! The Real Time Web RSS XMPP Twitter Jabber Ping FriendFeed ReverseHTTP SUP rssCloud Comet PubSubHubbub Push LongPoll OAuth Atom The Real Time Web The Problem (basic) Pull Pull Pull Flux CMS Pull Pull Pull Pull Pull The Real Time Web The Solution (basic) Push Push Push Push Flux CMS Push Hub Push Push Push Push The Real Time Web Task Publish stuff on a Flux CMS From many places Flux CMS To many places As open and as flexible as possible And as fast as possible (few seconds) The Real Time Web My Setup ... The Real Time Web The Publishing Part Web Admin via HTML/HTTP metaWebLog via XMLRPC Flux CMS Mail via SMTP Mail2Blog XMLRPC SMTP ReST GMaps Flickr The Real Time Web Obvious Web Admin via HTML/HTTP Flux CMS Via Web Admin The Real Time Web Old School metaWebLog via XMLRPC Flux CMS Via Weblog Client like Ecto The Real Time Web On the go Flux CMS Mail via SMTP Mail2Blog XMLRPC Via Mailclient The Real Time Web On the go Flux CMS Mail via SMTP Mail2Blog XMLRPC ReST Automatic Geotagging GMaps The Real Time Web On the go Flux CMS Mail via SMTP Mail2Blog XMLRPC SMTP ReST And forward directly to Flickr GMaps Flickr The Real Time Web The Notifying Part Ping y Notifi SUP Flux Notifiy CMS Notifi y PubSubHubbub Hello The Real Time Web Ping - weblogs.com Ping “Very” old y XML-RPC or ReST Notifi Flux Pull for Consumers CMS Used by blogug and many others The Real Time Web Simple Update Protocol “Invented” by FriendFeed Still Pull for Consumers SUP Flux Notifiy Privacy enabled CMS Compact Well suited for large “providers” like flickr Advertise via RSS Feed or HTTP Header The Real Time Web PubsubHubbub “Invented” by Google Employees Pretty young Flux Push for all CMS Notifi y Anyone can be a Hub PubSubHubbub Hello Advertise via RSS Feed rssCloud is something similar The Real Time Web The Pushing Part Pull blogugtechno Ping .
  • Easy Ways to Strengthen Your Online Identity White Paper Prepared by Jean Cummings, a Resume for Today Copyright © 2012

    Easy Ways to Strengthen Your Online Identity White Paper Prepared by Jean Cummings, a Resume for Today Copyright © 2012

    16 (Mostly) Easy Ways to Strengthen Your Online Identity White Paper Prepared by Jean Cummings, A Resume For Today Copyright © 2012 Greetings! Now that you have your professional marketing documents and LinkedIn Profile, it’s time to strengthen your online identity. This will help you a great deal in advancing your career! Most recruiters and hiring managers Google candidates names and check LI and social media for information (pro and con) before interviewing them. This plan will give you some ideas on how to show up on-brand in more places online. • “Search Me” Capability: Go to vizibility.com. Get a free link and put it in your LinkedIn profile and anywhere else where people might want to click through and find out more about you. Get a “Search Me” button for your website, if you have one. The button and link allow you to control the results if someone clicks on it. • Put profiles on ZoomInfo, Twitter and/or Facebook. You may also create a presence on any other site you want to be on. For instance: Ryze, Ning, Ziggs, Naymz, Business Card 2, spoke, plaxo, alias, friendfeed, ecademy, friendfeed. Having a presence on several of these sites will help you show up in a Google search. • Google Profiles: https://profiles.google.com/ Create a Google profile for yourself that will give you another place to be found on the web. • Google Docs: Put up your resume on Google docs and set it to allow general search. • Build a personal portal on the Web: Go to http://about.me/ and build a free profile with links to places where you have a significant presence on the Web, including LinkedIn, Twitter, Facebook, Zoominfo, etc.
  • Facebook Marketing, Twitter

    Facebook Marketing, Twitter

    Suite 220-309 McDermot Ave. | Winnipeg, MB, Canada R3A 1T3 Phone: 204.943.3923 | Fax: 204.943.4197 | Toll Free: 1.866.882.3580 Recognition Brought To Life Web: www.plannedlegacy.com | Email: [email protected] Facebook Marketing, Twitter - Social Marketing Interview Mari Smith - MariSmith.com Relationship Marketing Specialist, Facebook, Twitter and Social Media Business Coach Free Facebook Tips About Mari: Dubbed the “Pied Piper of Facebook” by FastCompany.com, Mari Smith is a Relationship Marketing Special- ist and Social Media Business Coach. She helps businesses, entrepreneurs and nonprofits accelerate their profits using an integrated social marketing strategy, with particular focus on Facebook and Twitter. Mari is passionate about showing fellow professionals how to develop powerful profitable relationships using social media. _________________________________________________________________________________________________ Would you prefer advanced live Facebook training from Mari via the Web? If so, make sure to check out: http://www.quickstartsocialmedia.com _________________________________________________________________________________________________ Interview by George Williams, Communications Specialist, Planned Legacy Planned Legacy: Mari, you’ve risen to the top of your profession as a Facebook/Twitter Relationship Marketing Specialist and have become very well known as one of the leading social marketing experts in the world. Can you please tell us a little bit about yourself and how you came to be where you are today? Mari Smith: Well, I have always had a passion and an interest in the world of relationships, and in the early 2000s I studied and got certified as a relationship coach. At the time my field was predominantly concerned with personal relation- ships. I taught classes for singles and provided coaching for couples and singles and studied in depth the different types of assessments, particularly personality assessments.
  • Simplify Facebook and Twitter with the Abcs of Social Media

    Simplify Facebook and Twitter with the Abcs of Social Media

    Simplify Facebook and Twitter with the ABC’s of Social Media by Mari Smith – Social Media Consultant, Speaker & Trainer I created this five-part model - the ABC’s of Social Media - to simplify your social media efforts. Many folks come to social media and attempt to short-circuit the process by over-automating, over- broadcasting, over-delegating and miss out the vital component of connecting, engaging and building relationships. Once you’ve confirmed your target market uses Facebook and Twitter, and you’re happy with your brand, messaging and systems for capturing leads, etc., here’s how the ABC system works: Automate First, set up systems to automate your broadcasts, feeds, updates, content. On Twitter, this could be using Twitterfeed to automatically post your blog feed as tweets. To pre-schedule tweets, use TweetLater and/or Hootsuite. To update multiple social media sites, including Twitter, your Facebook personal profile, multiple Facebook Fan Pages, LinkedIn, FriendFeed and many more, Ping.fm works extremely well. To pre-schedule updates to multiple social sites, the best choice is HootSuite > Ping.fm. Import your blog post on Facebook using the Notes app and/or the Networked Blogs app. Aggregate all your social feeds into FriendFeed and add the FriendFeed app to your Facebook Profile. There are many more ways to automate; these are a great start. However, I do recommend not over-automating. Pre-scheduling and auto-broadcasting are great to ensure you at least have some content going out daily. But you’ll also want to generate real-time content/broadcasts too. Broadcast Broadcasts are what I call regular tweets (as opposed to @ replies), Facebook status updates, posts on Facebook, blog posts and more.
  • The Perfect Startup Five Keys to Their Unprecedented Success LOGO

    The Perfect Startup Five Keys to Their Unprecedented Success LOGO

    the Perfect Startup Five keys to their unprecedented success LOGO . …………………………………………………………….………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….. © faberNovel 2012 ••• 1 ••• A quick word from Stéphane Distinguin ( CEO – faberNovel ) …………………………………………………………….…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………... It is not about the IPO the entire world is We all know Facebook. commenting on: an IPO is certainly not an We all know Mark Zuckerberg. end, but a point on a startup trajectory. These are the names. It’s all about perfection. Up to now, Facebook has been the perfect startup, not because it has We all know the figures, succeeded in everything, but because its has built they are mind-blowing: more than itself step by step, in a very pragmatic way. There is no miracle here: it is just a startup that has had an 900 million members for a current almost perfect stance towards every challenge it has valuation of over 100 billion dollars. faced to date, and has built the most fascinating innovation platform. At first sight, we all knew Facebook Perfection is rarely encountered and even harder to would be the biggest start up "after analyze… At faberNovel, as we do every year, and Google", Mark Zuckerberg, the Social after Google, Apple and Amazon, we wanted to Network hero, a juvenile CEO signing highlight what could be the formula behind Facebook's roaring success. Is there a formula for perfection? We autographs and striking the pose like would like to share our discoveries, our dissection of one of the biggest rock stars of his this marvel just to make it more useful for all of you. I generation. am not sure perfection will be achievable for all of us, but it certainly is our best source of inspiration.
  • An Exploratory Study

    An Exploratory Study

    Online Journal of Communication and Media Technologies Volume: 6 – Issue: 3 July - 2016 Understanding Likes on Facebook: An Exploratory Study Anita Basalingappa. MICA, Ahmedabad, India M S Subhas, Karnatak University, India Ms Rashmi Tapariya, MICA, Ahmedabad, India Abstract This study focuses on understanding ‘likes’ on facebook. It is important to understand this phenomenon by studying how users would react to a post that is posted by a ‘friend’. Therefore, the objective of this paper is to understand what does it mean to ‘like’ a post on facebook? Is there a preference for a picture post in a status update than the written word? Are there distinctive types of ‘likes’ that can be useful to understand ‘likes’ on facebook? This is an exploratory study. Data was collected through Depth interviews and observing Facebook profiles. Twelve depth interviews were conducted and thirty nine facebook profiles’ data was observed from August 2013 to August 2014. The results show that profile pictures get maximum likes followed by status updates and then cover photos. The respondents indicated a pattern in the likes on each post. Keywords: social media, Facebook, like © Online Journal of Communication and Media Technologies 234 Online Journal of Communication and Media Technologies Volume: 6 – Issue: 3 July - 2016 Introduction Social media has become a power house as it provides for a democratic relationship. There is a need to understand this medium and its users on a continuous basis. Every new invention is a boon and a bane. Likewise, social media is one such powerful tool in marketing that is a boon and bane.
  • Characterization of Friendfeed – a Web-Based Social Aggregation Service

    Characterization of Friendfeed – a Web-Based Social Aggregation Service

    Characterization of FriendFeed – A Web-based Social Aggregation Service Trinabh Gupta Sanchit Garg Anirban Mahanti Niklas Carlsson Martin Arlitt IIT Delhi IIT Delhi NICTA University of Calgary HP Labs India India Australia Canada USA Abstract This paper studies the FriendFeed service, with emphasis on social aggregation properties and user activity patterns. Many Web users have accounts with multiple different social We are interested in understanding what types of services networking services. This scenario has prompted develop- users aggregate content from, who follows the aggregated ment of “social aggregation” services such as FriendFeed that content, and why. We are also interested in understanding aggregate the information available through various services. Using five weeks of activity of more than 100,000 Friend- the characteristics of the FriendFeed social network and how Feed users, we consider questions such as what types of ser- they relate to the characteristics of the social network ser- vices users aggregate content from, the relative popularity of vices that it aggregates. Note that FriendFeed being an ag- services, who follows the aggregated content feeds, and why. gregation service enables us to study different services from one common observation point, and allows us to get a unique “sneak peek” on how these social networking and content Introduction sharing services are being used by a common set of users. We believe that social aggregation services are of interest to With the increasing popularity of online social networking information hungry Web users, especially those that like to (OSN) and content sharing services, many Web users have always stay connected with the ongoings in the cyber world.